Principal Investigator: Kumar, Santosh TR&D3: Translation of Temporally Precise mHealth via Efficient and Embeddable Privacy-aware Biomarker Implementations Lead: Dr. Emre Ertin, The Ohio State University; 10% effort (1.2 CM) Abstract: The mHealth Center for Discovery, Optimization & Translation of Temporally-Precise Interventions (the mDOT Center) will enable a new paradigm of temporally-precise medicine to maintain health and manage the growing burden of chronic diseases. The mDOT Center will develop and disseminate the methods, tools, and infrastructure necessary for researchers to pursue the discovery, optimization and translation of temporally- precise mHealth interventions. Such interventions, when dynamically personalized to the moment-to-moment biopsychosocial-environmental context of each individual, will precipitate a much-needed transformation in healthcare by enabling patients to initiate and sustain the healthy lifestyle choices necessary for directly managing, treating, and in some cases even preventing the development of medical conditions. Organized around three Technology Research & Development (TR&D) projects, mDOT represents a unique national resource that will develop multiple methodological and technological innovations and support their translation into research and practice by the mHealth community in the form of easily deployable wearables, apps for wearables and smartphones, and a companion mHealth cloud system, all open-source. TR&D3 will develop, validate and disseminate algorithms, tools and software/hardware designs for translation of temporally-precise mHealth interventions through resource efficient, real time, low-latency and privacy-aware implementation of an array of digital biomarkers that can be deployed at scale. Our approach is centered around a hierarchical computing framework that reduces the data into minimal modular abstractions called Micromarkers computed at the edge devices (Aim 1). Modular Micromarker abstractions are used to compress task-specific information relevant to biomarker computations at the edge devices while stripping nuisance variables such as hardware biases/drifts and background levels not pertinent to inference. Our hierarchical computing framework can be extended to implement high data rate sensor arrays at edge devices to be used at new point of care and ambulatory settings. This is accomplished through integrating a compressive sensing pre-processor to achieve signal acquisition in a resource constrained setting (Aim 2). Finally, TR&D3 will create computational mechanisms and a general biomarker privacy framework to enable participant control over the privacy-utility trade-offs during study design, data collection, and sharing of collected mHealth data for third party research when data cross trust domains (Aim 3). These technologies will be developed in collaboration with collaborative projects and will be disseminated to service projects to ensure that TR&D3 technologies can solve real problems facing the health research community and ensure the usability of these technologies by investigators who are external to the mDOT investigating team. TR&D3 will synergistically work in partnership with the other TR&D projects, the Training and Dissemination Core, and the Administration Core to maximize the societal impact of TR&D3 technologies. 1